Sorting support apparatus, sorting support system, sorting support method, and program

ABSTRACT

A sorting support apparatus is provided with: an input part that inputs a transmission image obtained by radiating an inspection target with electromagnetic waves; a storage part that stores a plurality of learning models optimized respectively for at least one article and being associated with an assumed usage condition; and a determination part that selects one of the learning models based on a specified usage condition and uses the learning model to determine whether or not the one or more articles is contained in the inspection target.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.16/971,715 filed on Aug. 21, 2020, which is a National Stage ofInternational Application No. PCT/JP2019/013547 filed on Mar. 28, 2019,claiming priority based on Japanese Patent Application No. 2018-066026(filed on Mar. 29, 2018) the content of which is hereby incorporated inits entirety by reference into this specification. The presentdisclosure relates to a sorting support apparatus, a sorting supportsystem, a sorting support method, and a program.

BACKGROUND

Among articles imported into Japan from abroad are various types ofprohibited articles and restricted articles. Prohibited articlesinclude, for example, firearms, explosives, narcotics and specifieddrugs. Inspection of these is carried out at customs at variouslocations and post offices that have a customs facility for foreign mail(referred to below as “foreign mail customs post office”).

Similar restrictions also apply to general goods within Japan.Additionally, there are articles such as lithium batteries, explosivesand the like which are prohibited from being transported by air, ashazardous materials for air transportation. These inspections arecarried out at customs offices or distributor locations.

X-ray scanning apparatuses are used in these inspections. For example,Patent Literature (PTL) 1 discloses a system, a method, a device and anapparatus for determining whether an individual (22) is carrying asuspicious concealed object (25) in their clothing. The same literaturediscloses detecting a suspicious concealed object (25) throughinspection by electromagnetic radiation in a range of 200 MHz-1 THz, bya process of receiving image data corresponding to the intensity ofreflected radiation and depth difference of a reflecting surface.

[PTL 1]

Japanese Patent Kohyo Publication No. 2007-517275A

SUMMARY

At customs, besides X-ray scanning, inspections are carried out at thesame time with drug-sniffing dogs, and from the viewpoint of whether theweight and size of declared contents are in conformity. However, inrecent years prohibited articles and restricted articles are cleverlycarried, and in such cases, it is necessary to rely on inspections bystaff, so that staff work load is increasing.

It is an object of the present disclosure to provide a sorting supportapparatus, a sorting support system, a sorting support method, and aprogram, that can contribute to reducing the load of inspection work inthe abovementioned logistics process.

According to a first aspect, the disclosure provides a sorting supportapparatus comprising: an input part that inputs a transmission imageobtained by radiating an inspection target with electromagnetic waves; astorage part that stores a plurality of learning models optimizedrespectively for at least one article and being associated with anassumed usage condition; and a determination part that selects one ofthe learning models based on a specified usage condition and uses thelearning model to determine whether or not the one or more articles iscontained in the inspection target.

According to a second aspect, the disclosure provides a sorting supportsystem wherein the sorting support apparatus is disposed at multiplestages to determine in a stepwise manner whether or not the at least onearticle is included, using different learning models.

According to a third aspect, the disclosure provides a sorting supportmethod, wherein a sorting support apparatus comprising an input partthat inputs a transmission image obtained by radiating an inspectiontarget with electromagnetic waves, and a storage part that stores aplurality of learning models optimized respectively for at least onearticle and being associated with an assumed usage condition, selectsone of the learning models based on a specified usage condition, anddetermines, by using the learning model, whether or not the one or morearticles is included in the inspection target. The method is associatedwith a particular machine that is a sorting support apparatus which isprovided with an input part, a storage part and a processor thatimplements various processing steps.

According to a fourth aspect, the disclosure provides a program thatcauses a computer, installed in a sorting support apparatus comprisingan input part that inputs a transmission image obtained by radiating aninspection target with electromagnetic waves, and a storage part thatstores a plurality of learning models optimized respectively for atleast one article and being associated with an assumed usage condition,to execute processing comprising selecting one of the learning modelsbased on a specified usage condition, and determining, by using thelearning model, whether or not the one or more articles is included inthe inspection target. It is to be noted that this program may berecorded on a computer-readable (non-transitory) storage medium. Thatis, the present disclosure may be embodied as a computer programproduct. The program may be inputted via an input apparatus orcommunication interface from outside to a computer apparatus, stored inthe storage apparatus, and activated according to prescribed steps orprocesses of a processor. The program may display a processing resultthereof including an intermediate state as necessary via a displayapparatus for each stage, or may communicate with the outside via acommunication interface. A computer apparatus for this is typicallyprovided with, as an example, a processor that can be interconnectedtherewith by a bus, a storage apparatus, an input apparatus, acommunication interface, and a display apparatus as necessary.

DISCLOSURE

The meritorious effects of the present disclosure are summarized asfollows.

The disclosure can contribute to reducing the load required ininspection work in the abovementioned logistics process. That is, thepresent disclosure transforms a sorting support apparatus described inthe background technology into one that may dramatically reduce staffwork load.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a configuration of an exampleembodiment of the present disclosure.

FIG. 2 is a diagram showing an overall configuration of a systemincluding a sorting support apparatus in a first example embodiment ofthe disclosure.

FIG. 3 is a functional block diagram showing a configuration of thesorting support apparatus in the first example embodiment of thedisclosure.

FIG. 4 is a diagram showing an example of a set of learning models heldby the sorting support apparatus in the first example embodiment of thedisclosure.

FIG. 5 is a flowchart representing operations of the sorting supportapparatus in the first example embodiment of the disclosure.

FIG. 6 is a diagram showing an example of a set of learning models heldby the sorting support apparatus in a second example embodiment of thedisclosure.

FIG. 7 is a functional block diagram showing a configuration of thesorting support apparatus in a third example embodiment of thedisclosure.

FIG. 8 is a diagram showing an example of a set of learning models heldby the sorting support apparatus in the third example embodiment of thedisclosure.

FIG. 9 is a flowchart representing operations of the sorting supportapparatus in the third example embodiment of the disclosure.

FIG. 10 is a diagram showing an overall configuration of a systemincluding the sorting support apparatus in a fourth example embodimentof the disclosure.

FIG. 11 is a diagram showing a configuration of a computer configuringthe sorting support apparatus of the disclosure.

PREFERRED MODES

First, a description is given of an outline of an example embodiment ofthe present disclosure, making reference to the drawings. It is to benoted that reference symbols in the drawings attached to this outlineare added to respective elements for convenience as examples in order toaid understanding, and are not intended to limit the present disclosureto modes illustrated in the drawings. Connection lines between blocks inthe diagrams referred to in the following description include bothunidirectional and bidirectional. Unidirectional arrows schematicallyshow flow of main signals (data), but do not exclude bidirectionality.There are ports and interfaces at input output connection points ofrespective blocks in the diagrams, but illustrations thereof areomitted. A program is executed via a computer apparatus, and thecomputer apparatus is provided with, for example, a processor, a storageapparatus, an input apparatus, a communication interface, and a displayapparatus as necessary. The computer apparatus is configured to enablecommunication, either wireless or wired, with equipment (including acomputer) within or outside the apparatus via a communication interface.

The present disclosure, in an example embodiment thereof as shown inFIG. 1 , may be implemented as a sorting support apparatus 10 providedwith an input part 11, a storage part 12 and a determination part 13.More specifically, the input part 11 inputs a transmission image from anapparatus that obtains the transmission image by radiating an inspectiontarget with electromagnetic waves.

The storage part 12 stores a plurality of learning models A to C,associated with assumed usage conditions and optimized respectively forat least 1 article. The learning models A to C may be created using AI(Artificial Intelligence) as represented by deep learning, usingtransmission images of the respective articles as instructor data.

The determination part 13 selects one of the learning models, based on aspecified usage condition, uses the learning model to determine whetheror not the at least one article is included in the inspection target,and outputs a result thereof.

The sorting support apparatus of the present disclosure is installed atvarious sites where inspection of contents of goods is needed. Articlesfor which detection is anticipated by the present apparatus aredifferent at each of these sites. Therefore, by creating theabovementioned learning models A to C, using these articles for whichdetection is anticipated as learning data with instructor, it ispossible to perform sorting support specialized for each site.

For example, for a certain logistics operator, in a case where there isconcern about a small sized electrical appliance received with abuilt-in lithium battery, a learning model is prepared that is optimizedto enable a lithium battery or small sized electrical appliance to besuitably detected. At an air transport goods inspection site of thelogistics operator, the sorting support apparatus makes a determinationregarding the inspection target by using this learning modelcorresponding to the usage condition (lithium battery detection).

For example, in the same way for a certain logistics operator, in a casewhere there is concern about goods being receiving that contain a livingcreature such as an insect or a small animal, a learning model isprepared that is optimized to enable these living creatures to besuitably detected. At a goods inspection site of the logistics operator,the sorting support apparatus makes a determination regarding theinspection target by using this learning model corresponding to theusage condition (living creature detection).

It is to be noted that in selection of the learning model, a user of thesorting support apparatus 10 may explicitly specify a usage condition,or the sorting support apparatus 10 may determine a usage conditionbased on information inputted by the user of the sorting supportapparatus 10, to select the learning model.

As described above, according to the present disclosure, it is possibleto improve the accuracy of sorting by the sorting support apparatus 10,and to reduce misjudgments. A reason for this is due to the employmentof a configuration in which selection is performed of a learning modelused in the sorting support apparatus 10 based on a specified usagecondition.

<First Exemplary Embodiment>

Continuing, a detailed description referring to the drawings is givenconcerning a first example embodiment of the present disclosure assumingsupporting an inspection operation on a post office article at a foreignmail customs post office, using an X-ray as an electromagnetic wave forobtaining a transmission image. FIG. 2 is a diagram showing an overallconfiguration of a system including a sorting support apparatus of thefirst example embodiment of the disclosure. FIG. 2 shows the sortingsupport apparatus 100 in a form straddling a belt conveyor 190 thatmoves a package 300. In the example of FIG. 2 the sorting supportapparatus 100 is connected to a rotating light 170 and to an operationterminal 180.

In a case of determining that a specific article is included in thepackage 300 that is an inspection target, the sorting support apparatus100 activates the flashing light 170 and also performs an operation tostop the belt conveyor 190. Clearly, it is also possible to employ amode where, instead of the flashing light 170, a speaker is connectedand a buzzer or a voice announcement is outputted. Instead of these itis possible to employ a mode in which a warning is displayed on aconsole or dashboard (information display program) or the like on adisplay screen of an operation terminal. An inspection staff memberaccepts these outputs, confirms whether or not the weight of the package300 matches the contents, and performs a detailed inspection by openingit. In order to reduce the workload of the inspection staff member inthese processes, an apparatus which automatically sorts a packagedetermined to include a specific article may be disposed therebeside.

The operation terminal 180 is an apparatus such as a personal computer(PC) used for setting a learning model, described later, in the sortingsupport apparatus 100, or for setting a usage condition of the sortingsupport apparatus 100. Using the operation terminal 180, the sortingsupport apparatus 100 may be enabled to set an operation in a case of adetermination that a specific article is included in the package.

FIG. 3 is a functional block diagram showing a configuration of thesorting support apparatus in the first example embodiment of thedisclosure. FIG. 3 shows the sorting support apparatus 100 provided withan input part 101, a storage part 102 and a determination part 103.

When an X-ray image is inputted from an X-ray camera 104 disposed insidean internal casing of a sorting support apparatus, the input part 101outputs to the determination part 103. At this time, the input part 101may perform essential pre-processing in order to improve determinationaccuracy of the determination part 103.

The storage part 102 stores a plurality of learning models that areassociated with assumed usage conditions and optimized respectively forat least 1 article. The learning models may be created using AI asrepresented by deep learning, using a large amount of actually obtainedX-ray images as instructor data.

FIG. 4 is an example of a set of learning models according to location(sorting site) stored in the storage part 102. A learning model A inFIG. 4 is a learning model created assuming usage at a foreign mailcustoms post office (this type of foreign mail customs post office isdescribed below as “foreign mail customs post office A”) handling alarge amount of surface mail. Specifically, the learning model A iscreated using image data of various types of prohibited articles andrestricted articles actually found at a foreign mail customs post officeA, to calculate a characteristic amount thereof. Similarly, a learningmodel B is a learning model created assuming usage at a foreign mailcustoms post office (described below as “foreign mail customs postoffice B”) handling a large amount of air mail or EMS (Express MailService). Specifically, the learning model B is created using image dataof various types of prohibited articles and restricted articles actuallyfound at a foreign mail customs post office B, to calculate acharacteristic amount thereof. There is a great variety of packages(post office articles) that arrive at foreign mail customs post offices,but by systematic elimination of such learning models, it is possible toimprove detection accuracy of specific articles and to reducemisjudgment rates, without improving the required performance of thesorting support apparatus. It is to be noted that clearly a learningmodel outside of the learning models A and B may be provided, accordingto differences of articles handled at each foreign mail customs postoffice.

It is to be noted that the learning model SP of FIG. 4 is a learningmodel that assumes prioritized inspection articles. For example, at eachcustoms point, a time-period is established and strengthening ofinspection of specific articles is carried out. The learning model SP isa learning model created in order to strengthen the inspection ofarticles set as prioritized inspection articles. By using this type oflearning model SP alone, or using learning models A and B together, itis possible to handle strengthening of inspection of prioritizedinspection articles. It is to be noted that the set of learning modelsshown in FIG. 4 is merely an example thereof, and the set of learningmodels may be changed according to location (sorting location).

The determination part 103 selects a learning model based on a usagecondition of the sorting support apparatus 100 inputted by the operationterminal 180 and performs inspection of a package. For example, thedetermination part 103 uses the learning model to inspect an X-ray imageand calculate the probability (likelihood) that a specified article isincluded in the inspection target. In a case where the probability(likelihood) is greater than or equal to a prescribed threshold, thedetermination part 103 determines that the article is included in theinspection target, and activates the rotating light 170 along withperforming an operation to stop the belt conveyor 190.

Continuing, a detailed description referring to the drawings is givenconcerning operations of the present example embodiment. FIG. 5 is aflow chart representing operations of the sorting support apparatus inthe first example embodiment of the disclosure. Referring to FIG. 5 ,first, a learning model is selected at the operation terminal 180 and aninspection is started (step S001). In this way, the belt conveyor 190 isactivated and an image taken by the X-ray camera 104 is sent to thesorting support apparatus 100.

When the X-ray image is inputted (step S002), the sorting supportapparatus 100 uses the selected learning model to confirm whether or nota specified article is included in the X-ray image (step S003). Here, ina case where it is determined that a specified article is included inthe X-ray image (YES in step S004), the sorting support apparatus 100activates the rotating light 170, along with performing an operation tostop the belt conveyor 190 (step S005).

An inspection staff member who receives the notification performs adetailed check of a package 300 for which a determination has been madethat a specified article is included in the X-ray image, and performs aninspection to open the package as necessary.

Meanwhile, in a case where it is determined that a specified article isnot included in the X-ray image (step S004), the sorting supportapparatus 100 continues with input of an X-ray image and making adetermination until the inspection is finished (continuation steps S002to S006).

It is to be noted that to finish the inspection, a determination may bemade, for example, by input of an end-operation by the operationterminal 180 or a prescribed time being reached.

As described above, according to the present example embodiment it ispossible to dramatically improve the determination accuracy of thesorting support apparatus. A reason for this is that a configuration isemployed in which a plurality of learning models are provided and aselection to be used is made in accordance with a usage condition.

Another aspect of the improvement in determination accuracy according tothe present example embodiment is that misjudgments can be reduced.

<Second Exemplary Embodiment>

Continuing, a detailed description referring to the drawings is givenconcerning a second example embodiment in which a modification is addedto the set of learning models stored in the storage part 102. Since thesecond example embodiment can be implemented by a configuration similarto the first example embodiment, a description is given below centeredon points of difference.

FIG. 6 is an example of a set of learning models stored in the storagepart 102 of the sorting support apparatus 100 of the second exampleembodiment. A point of difference from the set of learning models shownin FIG. 4 is that, for the same foreign mail customs post office, aplurality of learning models are provided, giving consideration to theseason and time-period.

A learning model A1 in FIG. 6 is a learning model created assuming usagein the Christmas season at a foreign mail customs post office A thathandles a large amount of surface mail. Specifically, the learning modelA1 is created using image data of prohibited articles and restrictedarticles prepared giving consideration to trends among articles actuallyhandled in the Christmas season, at the foreign mail customs post officeA. Similarly, a learning model A2 is created using image data ofprohibited articles and restricted articles prepared givingconsideration to trends among goods actually handled outside theChristmas season, at the foreign mail customs post office A. In thisway, by using learning models according to the Christmas season and toother seasons, it is possible to perform sorting giving consideration toa rapid increase in greeting cards or presents in the Christmas season.In other seasons, by using a learning model that does not giveconsideration to the presence of greeting cards and presents, it ispossible to improve sorting accuracy.

A learning model B1 in FIG. 6 is a learning model created assuming usagein January to March at a foreign mail customs post office B. Similarly,a learning model B2 is a learning model created assuming usage in Aprilto December at a foreign mail customs post office B. For example, in theAsian region, since there is a change in articles handled at the Chinesenew year, using this type of learning model is effective.

As described above, according to the present example embodiment it ispossible to perform sorting support giving consideration to change inpackages according to season and time-period. It is to be noted that inthe abovementioned description, learning models are used according toseason and time-period at foreign mail customs post office units, as inforeign mail customs post offices A and B, but it is also possible toemploy a configuration that does not consider differences of foreignmail customs post offices, but provides learning models according to theseason and according to the time-period, to jointly use learning modelsat a plurality of foreign mail customs post offices.

<Third Exemplary Embodiment>

Continuing, a detailed description referring to the drawings is givenconcerning a third example embodiment in which learning models are usedgiving consideration to sender location (remitter location). Since thethird example embodiment can basically be implemented by a configurationsimilar to the first example embodiment, a description is given belowcentering on points of difference.

FIG. 7 is a functional block diagram showing a configuration of asorting support apparatus 100 a in the third example embodiment of thedisclosure. A point of difference from the sorting support apparatus 100of the first example embodiment shown in FIG. 3 is that a senderlocation recognition part 105 is added, and a determination part 103selects a learning model using a sender location recognized by thesender location recognition part 105.

The sender location recognition part 105 recognizes the sender locationfrom a tag, an addressee label, an airline sticker, a barcode or thelike, attached to a package 300. It is to be noted that characterrecognition technology may be used as a method of recognizing the senderlocation. Besides the method of recognizing sender location directlyfrom the tag or the like, it is also possible to adopt a configurationin which the package 300 is identified from tracking information orquery number of the package 300, and to send a query about the senderlocation to an external server or the like.

FIG. 8 is an example of a set of learning models stored in the storagepart 102 of the sorting support apparatus 100 a of the third exampleembodiment. A point of difference from the set of learning models shownin FIG. 4 is that, for the same foreign mail customs post office, aplurality of learning models are provided according to sender location.

A learning model AA in FIG. 8 is a learning model created assuming as atarget a package with country A as a sender location, at foreign mailcustoms post office A that handles a large amount of surface mail.Specifically, the learning model AA is created using image data ofprohibited articles and restricted articles actually found in a packagesent from country A at a foreign mail customs post office. Similarly, alearning model AB is created using image data of prohibited articles andrestricted articles actually found in a package sent from country B atthe foreign mail customs post office A. Similarly, a learning model AXis created using image data of prohibited articles and restrictedarticles actually found in a package sent from somewhere other thancountry A and country B, at the foreign mail customs post office A. Inthis way, by using learning models according to sender location, it ispossible to perform sorting giving consideration to trends in packagessent from respective countries.

The determination part 103 in the present example embodiment selects alearning model based on sender location information obtained at thesender location recognition part 105, in addition to a usage conditionof the sorting support apparatus 100 a inputted by the operationterminal 180, and performs an inspection of the package.

FIG. 9 is a flow chart representing operations of the sorting supportapparatus 100 a in the present example embodiment. Steps S011 and S012are added to FIG. 5 that shows operations of the first exampleembodiment. That is, the sorting support apparatus 100 a performsautomatic recognition of sender location, after input of an X-ray image(step S011). It is to be noted that in the example of FIG. 9 , automaticrecognition of sender location is performed after input of an X-rayimage, but the automatic recognition of the sender location may also beperformed first.

The sorting support apparatus 100 a performs reselection of a learningmodel giving consideration to the recognized sender location (stepS012). Since subsequent operations are similar to the first exampleembodiment, a description is omitted.

As described above, according to the present example embodiment, it ispossible to perform sorting support giving consideration to change inpackages according to difference of sender location. It is to be notedthat in the abovementioned description, learning models are usedaccording to sender location in foreign mail customs post office units,as in foreign mail customs post offices A and B, but it is also possibleto employ a configuration that does not consider differences of foreignmail customs post offices, but provides learning models according tosender location, to jointly use learning models at a plurality offoreign mail customs post offices.

<Fourth Exemplary Embodiment>

Continuing, a description is given of a fourth example embodimentcombining the sorting support apparatuses of the first to third exampleembodiments described above, to perform sorting of packages in astepwise manner. FIG. 10 is a diagram showing an overall configurationof a system including a sorting support apparatus in the fourth exampleembodiment of the disclosure. For example, in a sorting supportapparatus 100-1 at a first stage, inspection of a package is performedusing a learning model B directed toward foreign mail customs postoffice B. In a sorting support apparatus 100-2 at a second stage,inspection of a package is performed using a learning model SPspecialized for inspection of prioritized inspection articles.

For example, as shown in FIG. 10 , the sorting support apparatus 100-1uses the learning model B to inspect miscellaneous packages (P1 to P3),and sorts a package (P3 in FIG. 10 ) that may contain an article outsideof paper. The sorting support apparatus 100-2 uses a learning model SP,inspects only a package (P3 in FIG. 10 ) that may contain an articleoutside of paper, and performs a determination from a viewpoint as towhether or not it contains a prioritized inspection article.

According to the fourth example embodiment with the above type ofmulti-stage configuration, it is possible to focus on the number ofpackages sent to the sorting support apparatus that performs a moredifficult inspection and determination, and to send the package to asorting support apparatus that uses a learning model that performs amore sensitive inspection. This sensitive inspection can realizechanging of determination threshold even where the same learning modelis used. For example, in a calculation using a learning model, in a caseof a probability of 60% or more of a determination that a suspectarticle is contained, instead of a normal-time operation determiningthat there is a specific article, the threshold may be changed so asmake a determination that there is a specific article in a case wherethis probability is 50% or more.

In the example of FIG. 10 , a 2 stage configuration with a sortingsupport apparatus 100-1 and a sorting support apparatus 100-2 isassumed, but a multi-stage inspection with 3 or more stages may also beperformed, using 3 or more sorting support apparatuses.

From a different viewpoint, it is also possible to employ aconfiguration in which a high risk article is detected first. Forexample, sorting support apparatuses that perform inspection using alearning model optimized for detection of “powder” and “pills” that havea high probability of being drugs, may be disposed in a first stage ofmultiple stages. From a similar viewpoint, sorting support apparatusesthat perform inspection using a learning model optimized for detectionof articles such as a “knife” or a “brand name article” for which visualinspection is required, may be disposed in any stage of multiple stages.In addition, low risk articles outside of paper may be added to excludedtargets in the sorting support apparatus at a first stage.

A description has been given above of respective example embodiments ofthe present disclosure, but the present disclosure is not limited to theabovementioned example embodiments, and modifications, substitutions andadjustments may be added within a scope that does not depart fromfundamental technical concepts of the disclosure. For example, networkconfigurations, respective element configurations and message expressionmodes shown in the respective drawings are examples for the purpose ofaiding understanding of the disclosure, and are not intended to limitthe disclosure to configurations illustrated in the drawings. In thefollowing description, “A and/or B” is used to indicate at least 1 of Aor B.

For example, in each of the abovementioned example embodiments adescription has been given centered on using a plurality of learningmodels in accordance with usage condition, but it is also possible tochange determination threshold in the determination part 103 describedabove, in accordance with usage condition. For example, in a case ofusing learning model A, which assumes usage at foreign mail customs postoffice A, at foreign mail customs post office A, a determinationthreshold of 50% is assumed as a threshold for determining whether ornot 1 or more articles is included, but in a case of using learningmodel A at other foreign mail customs post offices, it is also possibleto have another value as the determination threshold. Similarly, it isalso possible to change not only the learning model but alsodetermination threshold in accordance with season or sender location,and to increase overall determination accuracy.

In the abovementioned respective example embodiments, a description isgiven in which sorting support apparatuses 100, 100 a, 100-1, 100-2select 1 learning model, but these sorting support apparatuses mayselect a plurality of learning models to perform determination. Forexample, by creating a learning model for each prohibited article, andthe sorting support apparatus 100 performing an inspection of theseprohibited articles in order, it is possible to implement the inspectionwithout compromising the organization.

In the abovementioned respective example embodiments, a description isgiven in which a usage condition of the sorting support apparatus 100 isset by the operation terminal 180, but modes in which the sortingsupport apparatus 100 specifies a usage condition are not limitedthereto. For example, instead of setting by the abovementioned operationterminal 180, it is possible to employ a mode in which a usage conditionis set according to transmission of a message or e-mail includinginformation for setting of usage condition to the sorting supportapparatus 100. For example, a configuration may also be employed inwhich the sorting support apparatus 100 itself makes a query to anetwork or information set therein, determines a usage conditionindirectly specifies by this information, and selects a learning model.

In the abovementioned respective example embodiments, a description isgiven citing examples of using the sorting support apparatus of thepresent disclosure in sorting a package at a foreign mail customs postoffice, but usage of the sorting support apparatus of the presentdisclosure is not limited to these examples. By providing a requiredlearning model, application is also possible, for example, to inspectionof packages at a collection location of a national distributor or anairline.

For example, a lithium battery specified as an airline hazardoussubstance may be enclosed with a small sized home appliance, butaccording to the present disclosure, in a check when an article isreceived at a shop or at an X-ray inspection at an airport, by using alearning model optimized for detecting a lithium battery from among aplurality of learning models, detection can be carried out efficiently.

The procedure illustrated in the abovementioned first to fourth exampleembodiments may be realized by a program that causes a computer (9000 inFIG. 11 ) functioning as the sorting support apparatus to realizefunctionality as a sorting support apparatus. Such a computer isexemplified in a configuration provided with a CPU (Central ProcessingUnit) 9010, a communication interface 9020, a memory 9030, and anauxiliary storage apparatus 9040, in FIG. 11 . Namely, a determinationprogram that uses a learning model or a selection program for a learningmodel may be executed in the CPU 9010 of FIG. 11 , and update processingof respective calculated parameters held in the auxiliary storageapparatus 9040 may be implemented.

That is, the respective parts (processing means, functions) of thesorting support apparatus illustrated in the abovementioned first tofourth example embodiments may be implemented by a computer program thatcauses the abovementioned respective processing to be executed in aprocessor installed in the sorting support apparatus, using hardwarethereof.

Finally, preferred modes of the present disclosure are summarized.

<First Mode>

(Refer to the sorting support apparatus according to the first aspectdescribed above.)

<Second Mode>

In the sorting support apparatus, the learning model is preferablycreated in accordance with a trend of handled goods at a location wherethe sorting support apparatus is disposed.

<Third Mode>

In the sorting support apparatus, the learning model is preferablycreated in accordance with a trend of handled goods at a time-periodwhen sorting is performed.

<Fourth Mode>

In the sorting support apparatus, the learning model is preferablycreated in accordance with a trend of handled goods according to senderlocation.

<Fifth Mode>

In the sorting support apparatus, it is preferable to be able to changea threshold for determining, in the determination part, whether or notthe at least one article is included.

<Sixth Mode>

(Refer to the sorting support system according to the second aspectdescribed above.)

<Seventh Mode>

(Refer to the sorting support method according to the third aspectdescribed above.)

<Eighth Mode>

(Refer to the program according to the fourth aspect described above.)It is to be noted that the abovementioned sixth to eighth modes may beexpanded with regard to the second to fifth modes, similar to the firstmode.

It is to be noted that the various disclosures of the abovementionedPatent Literature and Non-Patent Literature are incorporated herein byreference thereto. Modifications and adjustments of example embodimentsand examples may be made within the bounds of the entire disclosure(including the scope of the claims) of the present disclosure, and alsobased on fundamental technological concepts thereof. Variouscombinations and selections of various disclosed elements (includingrespective elements of the respective claims, respective elements of therespective example embodiments and examples, respective elements of therespective drawings and the like) are possible within the scope of thedisclosure of the present disclosure. That is, the present disclosureclearly includes every type of transformation and modification that aperson skilled in the art can realize according to the entire disclosureincluding the scope of the claims and to technological concepts thereof.In particular, with regard to numerical ranges described in the presentspecification, arbitrary numerical values and small ranges included inthe relevant ranges should be interpreted to be specifically describedeven where there is no particular description thereof.

REFERENCE SIGNS LIST

-   -   10 sorting support apparatus    -   11, 101 input part    -   12, 102 storage part    -   13, 103 determination part    -   100, 100 a, 100-1, 100-2 sorting support apparatus    -   104 X-ray camera    -   105 sender location recognition part    -   170 rotating light    -   180 operation terminal    -   190 belt conveyor    -   300, P1-P3 package    -   9000 computer    -   9010 CPU    -   9020 communication interface    -   9030 memory    -   9040 auxiliary storage apparatus

1. A sorting support apparatus comprising: at least one memory storing acomputer program; and at least one processor configured to execute thecomputer program to input a transmission image obtained by radiating aninspection target with electromagnetic waves; and determine whether ornot one or more articles used under a specified usage condition iscontained in the inspection target, wherein it is possible to change athreshold for determining whether or not the at least one article isincluded.
 2. The sorting support apparatus according to claim 1, whereinthe specified usage condition is associated with a trend of handledgoods at a location where the sorting support apparatus is disposed. 3.The sorting support apparatus according to claim 1, wherein thespecified usage condition is associated with a trend of handled goods ina time-period in which sorting is performed.
 4. The sorting supportapparatus according to claim 1, wherein the specified usage condition isassociated with a trend of handled goods according to sender location.5. A sorting support system wherein the sorting support apparatus ofclaim 1 is disposed at multiple stages to determine in a stepwise mannerwhether or not the at least one article is included, and wherein thespecified usage conditions in the sorting support apparatuses aredifferent each other.
 6. The sorting support system according to claim5, configured so that a sorting support apparatus at a first stage usesa usage condition for sorting paper and non-paper articles, and sortingsupport apparatuses at second and following stages use a usage conditionfor further sorting the non-paper articles.
 7. A sorting support methodcomprising: by a sorting support apparatus, inputting a transmissionimage obtained by radiating an inspection target with electromagneticwaves; and determining whether or not one or more articles used under aspecified usage condition is contained in the inspection target, whereinit is possible to change a threshold for determining whether or not theat least one article is included.
 8. The sorting support methodaccording to claim 7, wherein the specified usage condition isassociated with a trend of handled goods at a location where the sortingsupport apparatus is disposed.
 9. The sorting support method accordingto claim 7, wherein the specified usage condition is associated with atrend of handled goods in a time-period in which sorting is performed.10. The sorting support method according to claim 7, wherein thespecified usage condition is associated with a trend of handled goodsaccording to sender location.
 11. A sorting support method according toclaim 7, wherein the sorting support apparatus is one of sorting supportapparatuses disposed at multiple stages, and wherein the specified usageconditions in the sorting support apparatuses are different each other.12. The sorting support method according to claim 11, configured so thata sorting support apparatus at a first stage uses a usage condition forsorting paper and non-paper articles, and sorting support apparatuses atsecond and following stages use a usage condition for further sortingthe non-paper articles.
 13. A computer-readable non-transitory recordingmedium recording a program, the program causing a computer of a sortingsupport apparatus to perform, inputting a transmission image obtained byradiating an inspection target with electromagnetic waves; anddetermining whether or not one or more articles used under a specifiedusage condition is contained in the inspection target, wherein theprogram causes the computer to perform changing a threshold fordetermining whether or not the at least one article is included.
 14. Themedium according to claim 13, wherein the specified usage condition isassociated with a trend of handled goods at a location where the sortingsupport apparatus is disposed.
 15. The medium according to claim 13,wherein the specified usage condition is associated with a trend ofhandled goods in a time-period in which sorting is performed.
 16. Themedium according to claim 13, wherein the specified usage condition isassociated with a trend of handled goods according to sender location.17. A medium according to claim 13, wherein the sorting supportapparatus is one of sorting support apparatuses disposed at multiplestages, and wherein the specified usage conditions in the sortingsupport apparatuses are different each other.
 18. The medium accordingto claim 17, wherein the program causes the computer of a sortingsupport apparatus at a first stage to perform using a usage conditionfor sorting paper and non-paper articles.
 19. The medium according toclaim 17, wherein the program causes the computer of each of sortingsupport apparatuses at second and following stages to perform using ausage condition for further sorting the non-paper articles.